Publications by [R]eliability
Survival Analysis – Fitting Weibull Models for Improving Device Reliability in R
It’s time to get our hands dirty with some survival analysis! In this post, I’ll explore reliability modeling techniques that are applicable to Class III medical device testing. My goal is to expand on what I’ve been learning about GLM’s and get comfortable fitting data to Weibull distributions. I don’t have a ton of experience with Wei...
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Bayesian Stress-Strength Analysis for Product Design (in R and brms)
Whether you are building bridges, baseball bats, or medical devices, one of the most basic rules of engineering is that the thing you build must be strong enough to survive its service environment. Although a simple concept in principle, variation in use conditions, material properties, and geometric tolerances all introduce uncertainty that can ...
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Simulation With a Random Effects Model – Gage R&R as a Case Study
I’ve heard it said that common statistical tests are just linear models.1 It turns out that Gage R&R, a commonly used measurement system analysis (MSA), is no different. In this post I’ll attempt to provide some background on Gage R&R, describe the underlying model, and then walk through a method for simulation that can be useful for things l...
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Boundary Conditions and Anatomy – Exploring Correlated Data Simulation in R
Today we have been assigned the task of identifying boundary conditions for a benchtop durability test of an implantable, artificial heart valve. In other words, we need to identify credible parameters for a physical test such that our test engineers can challenge the device under severe but realistic geometries and loads. To facilitate this task...
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Tutorial – Design Study in Solidworks with Data Analysis in R
I decided to do something a little bit different with this post and show how R can be used in tandem with a traditional engineering CAD program. Together they comprise a streamlined and repeatable workflow that I’ve tried to leverage on my job when it makes sense to do so. Solidworks is a 3d CAD program that is used commonly in industry. One po...
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Exploring Frequentist and Bayesian Tolerance Intervals in R
Tolerance intervals are used to specify coverage of a population of data. In the frequentist framework, the width of the interval is dependent on the desired coverage proportion and the specified confidence level. They are widely used in the medical device industry because they can be compared directly vs. product specifications, allowing the en...
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Could AutoML win in the ‘Sliced’ Data Science Competition? The answer may shock you!
In this post I’ll be taking a break from my normal explorations in the medical device domain to talk about Sliced. Sliced is a 2-hour data science competition streamed on Twitch and hosted by Meg Risdal and Nick Wan. Four competitors tackle a prediction problem in real time using whatever coding language or tools they prefer, grabbing bonus poi...
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Trying to Trick Linear Regression – Estimating Coefficients for Variables in R
In this post we will try to trick linear regression into thinking that a redundant variable is statistically significant. By redundant, I mean a candidate predictor variable that in reality is just noise (no effect on the outcome) but that we might include in an experiment because we don’t know if it is important or not. The trick is that we ca...
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Durability Testing of Stents Using Sensitivity-Based Methods in R
The current industry protocol for durability testing of vascular stents and frames involves testing many implants simultaneously at a range of different stimulus magnitudes (typically strain or stress). The test levels are spread out like a grid across the stimulus range of interest. Each implant is tested to failure or run-out at its specified l...
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A Real World Use Case for a Bayesian Reliability Model – How to Incorporate FEA into Risk Estimates
Frequentist statistical methods, despite their flaws, are generally serviceable for a large suite of practical problems faced by engineers during product development of medical devices. But even in domains where simple models usually do the trick, there remain instances where a Bayesian approach is the best (and perhaps only logical) way to tackl...
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